map.py

   1 import matplotlib as mpl
   2 import matplotlib.mlab as mlab
   3 import matplotlib.pyplot as plt
   4 import matplotlib.axes as axe
   5 import matplotlib.collections as collections
   6 import numpy as np
   7 import random
   8 from pylab import *
   9 from matplotlib.pyplot import *
  10
  11 FIN = 'tmp.dat'
  12 FOUT = '../map_test.png'
  13
  14 samples = 10
  15 ticksize = 14
  16 labelsize = 24
  17 title = "a = 1.15, w = 0.1, F = 0.00, eta1 = 1.0, eta2 = 1.5, theta = 0"
  18 xlabel = r'$a$'
  19 ylabel = r'$l$'
  20 xtfreq = 15.2
  21 ytfreq = 1
  22 xpad = 0.001
  23 ypad = 0.001
  24 ms = 20
  25 ystep = 0.1
  26 xstep = 1
  27 res = (1440/80,900/80) # default dpi is 80
  28 method = 'direct' # mean, direct
  29
  30 ################################################################################
  31
  32 def assign_label(color):
  33         ret_label = 'DUMMY'
  34         if (color == 'grey'):
  35                 ret_label = r'$|v| = 0$'
  36         if (color == 'blue'):
  37                 ret_label = r'$|v| = -1$'
  38         if (color == 'red'):
  39                 ret_label = r'$|v| = 1$'
  40         if (color == 'orange'):
  41                 ret_label = r'$|v| = 0.5$'
  42         if (color == 'green'):
  43                 ret_label = r'$|v| = -0.5$'
  44         if (color == 'black'):
  45                 ret_label = r'other rational $v$'
  46         return (ret_label)
  47
  48 def assign_color2(colors,xar,yar,clist):
  49         print "---------------------------------------------------------"
  50         unique_colors = unique(colors)
  51         ret_xcolors = [ [] for DUMMYVAR in range(len(clist)) ]
  52         ret_ycolors = [ [] for DUMMYVAR in range(len(clist)) ]
  53         i = 0
  54         assert len(xar) == len(yar) == len(colors)
  55         xarlen_trunc = len(xar)
  56         while i < xarlen_trunc:
  57                 tmp = []
  58                 for j in range(samples):
  59                         assert i+j <= len(xar)
  60                         #assert yar[i+j] == yar[i] and xar[i+j] == xar[i]
  61                         tmp.append(colors[i+j])
  62 #               print len(tmp), samples
  63                 assert len(tmp) == samples
  64                 if 'green' in tmp and 'orange' in tmp:
  65                         ret_xcolors[6].append(xar[i])
  66                         ret_ycolors[6].append(yar[i])
  67                 elif 'blue' in tmp and 'red' in tmp:
  68                         ret_xcolors[7].append(xar[i])
  69                         ret_ycolors[7].append(yar[i])
  70                 elif len(unique(tmp)) == 1 and 'grey' in tmp:
  71                         ret_xcolors[1].append(xar[i])
  72                         ret_ycolors[1].append(yar[i])
  73                 elif len(unique(tmp)) == 1 and 'blue' in tmp:
  74                         ret_xcolors[2].append(xar[i])
  75                         ret_ycolors[2].append(yar[i])
  76                 elif len(unique(tmp)) == 1 and 'green' in tmp:
  77                         ret_xcolors[3].append(xar[i])
  78                         ret_ycolors[3].append(yar[i])
  79                 elif len(unique(tmp)) == 1 and 'orange' in tmp:
  80                         ret_xcolors[4].append(xar[i])
  81                         ret_ycolors[4].append(yar[i])
  82                 elif len(unique(tmp)) == 1 and 'red' in tmp:
  83                         ret_xcolors[5].append(xar[i])
  84                         ret_ycolors[5].append(yar[i])
  85                 else:
  86                         ret_xcolors[0].append(xar[i])
  87                         ret_ycolors[0].append(yar[i])
  88                 i = i+samples
  89                 #print "increment:",i
  90         for j  in range(8):
  91                 print clist[j],":",len(ret_xcolors[j])
  92         print "---------------------------------------------------------"
  93         return (ret_xcolors, ret_ycolors)
  94
  95 def assign_color(colors,xar,yar):
  96         print "---------------------------------------------------------"
  97         unique_colors = unique(colors)
  98         ret_xcolors = [ [] for DUMMYVAR in range(len(unique_colors)) ]
  99         ret_ycolors = [ [] for DUMMYVAR in range(len(unique_colors)) ]
 100         for j in range(len(unique_colors)):
 101                 tmpx = []
 102                 tmpy = []
 103                 k = 0
 104                 for i in range(len(colors)):
 105                         if (colors[i] == unique_colors[j]):
 106                                 k = k+1
 107                                 tmpxcolors = ret_xcolors[j]
 108                                 tmpycolors = ret_ycolors[j]
 109                                 #print tmpxcolors
 110                                 if len(tmpxcolors) > 0:
 111                                         if (tmpxcolors[-1] != xar[i]) or (tmpycolors[-1] != yar[i]):
 112                                                 #print tmpxcolors[-1],tmpycolors[-1],"is not", xar[i],yar[i],"!"
 113                                                 ret_xcolors[j].append(xar[i])
 114                                                 ret_ycolors[j].append(yar[i])
 115                                 else:
 116                                         ret_xcolors[j].append(xar[i])
 117                                         ret_ycolors[j].append(yar[i])
 118                 lenc = float(len(colors))
 119                 per = 100*k/lenc
 120                 print unique_colors[j],":",len(ret_xcolors[j]),k,"(",'%.2f' % per ,"%)"
 121         print "---------------------------------------------------------"
 122         return (ret_xcolors, ret_ycolors)
 123
 124 print "reading data..."
 125 r  = mlab.csv2rec(FIN, delimiter='\t', comments='#')
 126
 127 x = r.x
 128 x_color = r.vx_color
 129 y_color = r.vy_color
 130 y = r.y
 131 vx = r.vx
 132 vy = r.vy
 133
 134 assert len(x) == len(y) == len(vx) == len(vy)
 135 print "done."
 136
 137 xmin = np.min(unique(x))
 138 xmax = np.max(unique(x))
 139 ymin = np.min(unique(y))
 140 ymax = np.max(unique(y))
 141
 142 # assumes that the step distance doesn't change over the course of time
 143 rat = xstep/ystep
 144 print "size of xstep:", xstep
 145 print "size of ystep:", ystep
 146 print "aspect ratio:",rat
 147
 148 print "different values of vx:"
 149 print unique(vx),"(",len(vx),"total)"
 150
 151 print "different values of vy:"
 152 print unique(vy),"(",len(vy),"total)"
 153
 154 # needed for assign_color2
 155 color_list = [
 156                 'black',        # 0     everything else
 157                 'grey',         # 1     v = 0
 158                 'blue',         # 2     v = -1.0
 159                 'green',        # 3     v = -0.5
 160                 'orange',       # 4     v = 0.5
 161                 'red',          # 5     v = 1.0
 162                 'teal',         # 6     v = -0.5 && v = 0.5
 163                 'purple'        # 7     v = -1.0 && v = 1.0
 164         ]
 165
 166 label_list = [
 167                 r'other rational $v$',  # 0 black
 168                 r'$v = 0$',                             # 1 grey
 169                 r'$v = -1$',                    # 2 blue
 170                 r'$v = -0.5$',                  # 3 green
 171                 r'$v = 0.5$',                   # 4 orange
 172                 r'$v = 1.0$',                   # 5 red
 173                 r'$v = \pm 0.5$',               # 6 teal
 174                 r'$v = \pm 1.0$'                # 7 purple
 175         ]
 176
 177 xpadmin = xmin-xstep
 178 xpadmax = xmax+xstep
 179 ypadmin = ymin-ystep
 180 ypadmax = ymax+ystep
 181 xlim = (xpadmin, xpadmax)               # small padding between plot and
 182 ylim = (ypadmin, ypadmax)               # axis so they don't overlap
 183 xticks = np.arange(xmin, xmax+xpad, xtfreq)
 184 yticks = np.arange(ymin, ymax+ypad, ytfreq)
 185
 186
 187 fig = plt.figure(figsize=res)
 188 #fig.suptitle(title, size=labelsize)
 189
 190 max = 0.84
 191 c = max/2
 192 rect1 = [0.1, 0.1, c, 0.9]
 193 rect2 = [c+0.1, 0.1, c, 0.9]
 194
 195 ax1 = fig.add_axes(rect1, aspect=rat)
 196 ax2 = fig.add_axes(rect2, aspect=rat)
 197
 198 if (method == 'mean'):
 199
 200         print "assigning colors...."
 201         xcolors_x,xcolors_y = assign_color(x_color,x,y)
 202         ycolors_x,ycolors_y = assign_color(y_color,x,y)
 203         print "done."
 204
 205         print "plotting..."
 206         for k in range(len(unique(x_color))):
 207                 x_xtli = xcolors_x[k]
 208                 x_ytli = xcolors_y[k]
 209                 labelx = assign_label(unique(x_color)[k])
 210                 ax1.scatter(x_xtli, x_ytli, marker='s', lw=0, s=ms, alpha=0.5,
 211                                 c = unique(x_color)[k], label=labelx,
 212                                 edgecolor='face')
 213
 214         for k in range(len(unique(y_color))):
 215                 y_xtli = ycolors_x[k]
 216                 y_ytli = ycolors_y[k]
 217                 labely = assign_label(unique(y_color)[k])
 218                 ax2.scatter(y_xtli, y_ytli, marker='s', lw=0, s=ms, alpha=0.5,
 219                                 c = unique(y_color)[k], label=labely,
 220                                 edgecolor='none')
 221
 222         print "done."
 223
 224 elif (method == 'direct'):
 225
 226         print "assigning colors..."
 227         xcolors_x,xcolors_y = assign_color2(x_color,x,y,color_list)
 228         ycolors_x,ycolors_y = assign_color2(y_color,x,y,color_list)
 229         print "done."
 230
 231         #for i in range(8):
 232         #       print i, xcolors_x[i],xcolors_y[i];
 233
 234         print "plotting..."
 235         for k in range(len(xcolors_x)):
 236                 if len(xcolors_x[k]) > 0:
 237                         x_xtli = xcolors_x[k]
 238                         x_ytli = xcolors_y[k]
 239                         ax1.scatter(x_xtli, x_ytli, marker='s', lw=0,
 240                                         s=ms, alpha=1, c=color_list[k],
 241                                         label=label_list[k])
 242
 243         for k in range(len(ycolors_x)):
 244                 if len(ycolors_x[k]) > 0:
 245                         y_xtli = ycolors_x[k]
 246                         y_ytli = ycolors_y[k]
 247                         ax2.scatter(y_xtli, y_ytli, marker='s', lw=0,
 248                                         s=ms, alpha=1, c=color_list[k],
 249                                         label=label_list[k])
 250
 251         print "done."
 252 else:
 253         print "fatal error: specify a valid method"
 254
 255 leg1 = ax1.legend(bbox_to_anchor=(1.05, 1), loc=2, shadow=True,
 256                 fancybox=True, scatterpoints=1, markerscale=1,
 257                 borderaxespad=0.)
 258 leg2 = ax2.legend(bbox_to_anchor=(1.05, 1), loc=2, shadow=True,
 259                 fancybox=True, scatterpoints=1, markerscale=1,
 260                 borderaxespad=0.)
 261 for t in leg1.get_texts():
 262         t.set_fontsize('small')
 263 for t in leg2.get_texts():
 264         t.set_fontsize('small')
 265
 266 ax1.set_title(r'$v_x$', size=labelsize)
 267 ax1.set_xlim(xlim)
 268 ax1.set_ylim(ylim)
 269 ax1.set_xlabel(xlabel, size=labelsize)
 270 ax1.set_ylabel(ylabel, size=labelsize)
 271 ax1.set_xticks(xticks, minor=False)
 272 ax1.set_yticks(yticks, minor=False)
 273 ax1.set_xticklabels(ax1.get_xticks(), size=ticksize)
 274 ax1.set_yticklabels(ax1.get_yticks(), size=ticksize)
 275
 276 ax2.set_title(r'$v_y$', size=labelsize)
 277 ax2.set_xlim(xlim)
 278 ax2.set_ylim(ylim)
 279 ax2.set_xlabel(xlabel, size=labelsize)
 280 ax2.set_ylabel('', size=labelsize)
 281 ax2.set_xticks(xticks, minor=False)
 282 ax2.set_yticks(yticks, minor=False)
 283 ax2.set_xticklabels(ax2.get_xticks(), size=ticksize)
 284 ax2.set_yticklabels('', size=ticksize)
 285
 286 leg1.get_frame().set_alpha(0.75)
 287 leg2.get_frame().set_alpha(0.75)
 288
 289 plt.subplots_adjust(hspace=0)
 290 print "flushing into a file..."
 291 plt.savefig(FOUT)
 292 print "done."
 293 #plt.show()
 294
 295
 296
 297